The Bactra Review   The Computational Beauty of Nature
Chaitin's argument turns on the notion of Kolmogorov complexity (see my review of Badii and Politi's Complexity). In essence, it turns on the fact that a compression algorithm can't compress anything which is more complicated than itself. If one combines this result with the idea that the point of science is to produce concise descriptions of data-sets, and the fact that human beings certainly have a finite algorithmic information-content, we do in fact get what looks like strong limits on science.

Unfortunately for Chaitin, but fortunately for science, his notion of the acceptable kinds of descriptions is too restricted. Kolmogorov was, after all, aiming at describing randomness, and his complexity measure is maximized by sequences of independent random variables; but these can be extremely concisely described, as soon as we decide we don't care about the exact sequence and would prefer statistics. Since in Real Science we're stuck with statistics anyway, this really isn't much of a loss. (Flake acknowledges the importance of statistical descriptions --- pp. 132--4 --- but doesn't connect this to the methodological claims.)

Some of my research actually turns on trying to exploit these advantages of statistical over exact descriptions. See Cosma Rohilla Shalizi and James P. Crutchfield, "Computational Mechanics: Pattern and Prediction, Structure and Simplicity", Journal of Statistical Physics 104 (2001): 816--879 = arxiv:cond-mat/9907176.